to do before publish

#change all path to data showing my environment (NAS...) mettre lien à partir de Workspace ou il dowloaderont les fichier source (fastq et objet R)
#ajouter un test pour etre sur que y'ai subset 1 et 2 et pas que un des deux 

#do we want to show the code in rmd ? 

# GO mise en ligne des fichier brut

#test final sans aucun objet, avec le premier obj et avec le deuxieme

#change output path

[1] “You are starting the analysis from count matrix obtain with CellRanger and Cite-seq-count”

Loading the first experiment

Looking to HTO distribution accross sample :

#HTO distribution
par(mfrow=c(1,1))
par(las=2)
par(mar=c(5,15,3,3))
barplot(rowSums(hto1), main = "sequenced HTO distribution", horiz=TRUE)

rowSums(hto1)
##   Spleen-MP    Spleen-M Spleen-ctrl    Spleen-P   Thymus-MP    Thymus-M 
##      688432      206663      265412      662165      196262      117402 
## Thymus-ctrl    Thymus-P 
##      156856      562594

Demultiplexing HTO

Demultiplexing results

Cells classification

print (table(hashtag1@meta.data$MULTI_ID))
## 
##     Doublet    Negative Spleen-ctrl    Spleen-M   Spleen-MP    Spleen-P 
##         307          69         438         255         710         609 
## Thymus-ctrl    Thymus-M   Thymus-MP    Thymus-P 
##         325         245         294         935

Violinplot (features)

VlnPlot(hashtag1,features = c("nFeature_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

Violinplot (HTO counts)

VlnPlot(hashtag1,features = c("nCount_HTO"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

tSNEs based on HTO

# Calculate a distance matrix using HTO
hto.dist.mtx <- as.matrix(dist(t(GetAssayData(object = hashtag1, assay = "HTO"))))

# Calculate tSNE embeddings with a distance matrix
hashtag1 <- RunTSNE(hashtag1, distance.matrix = hto.dist.mtx, perplexity = 100)

HTO margin

Tsne<-data.frame(
  tSNE_1 = hashtag1@reductions$tsne@cell.embeddings[,1],
  tSNE_2= hashtag1@reductions$tsne@cell.embeddings[,2],
  gene= hashtag1@meta.data$HTO_margin
)

HTO= hashtag1@meta.data$MULTI_ID
Max=max(hashtag1@meta.data$HTO_margin)
Min=min(hashtag1@meta.data$HTO_margin)
ggplot(Tsne,aes(x=tSNE_1,y=tSNE_2))+geom_point(aes(color=gene,shape=HTO))+
           scale_colour_gradient2(low = "blue",mid="orange",high="red",name="HTO margin",midpoint=(Max+Min)/2)+scale_shape_manual(values = c(15,16,17,18,19,20,21,22,23,24,25))

Ridge plots

Visualize enrichment for selected HTOs with ridge plots

RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[1:2],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[3:4],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[5:6],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[7:8],ncol = 2, group.by = "MULTI_ID")

Sample Information

The analysis will be run on the sample 1 (181031).

During the sample loading, we filter cells that do not pass the following filters.
Here are the description of those parameters in the Seurat CreateSeuratObject function:

  • min.genes: Include cells where at least 200 genes are detected
  • min.cells: Include genes with detected expression in at least 3 cells

After those filters, the remaining cell number is `r length(colnames(hashtag1@assays$RNA@data)).

#add Exp1 cell identity
HTO_cr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-ctrl" ))
HTO_ct1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-ctrl" ))
HTO_mr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-M" ))
HTO_mt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-M" ))
HTO_pr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-P" ))
HTO_pt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-P" ))
HTO_pmr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-MP" ))
HTO_pmt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-MP" ))
HTO_d1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Doublet" ))
HTO_n1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Negative" ))

HTO_thymus1 = c(HTO_ct1,HTO_mt1,HTO_pt1,HTO_pmt1)
HTO_spleen1 = c(HTO_cr1,HTO_mr1,HTO_pr1,HTO_pmr1)
HTO_identified1 = c(HTO_thymus1, HTO_spleen1)


# Create a Seurat object without doublet and unassigned cells (remove "negative", "doublet" & "nothing"))
clean.subset1 <- subset(x = hashtag1, cells = HTO_identified1)
VlnPlot(clean.subset1,features = c("nFeature_RNA", "nCount_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

After removing doublets and negative cells, the remaining cell number is `r length(colnames(clean.subset1@assays$RNA@data)).

Mitochondrial percentage versus nFeatures

df<-data.frame(multi.id=Seurat1@misc$old_meta_data$MULTI_ID,percent.mito=Seurat1@misc$old_meta_data$percent.mito,nFeature_RNA=Seurat1@misc$old_meta_data$nFeature_RNA)
ggplotly(ggplot(df,aes(x=nFeature_RNA,y=percent.mito,color=multi.id))+geom_point())

UMAP

ggplotly(DimPlot(Seurat1, reduction = "umap", group.by = "MULTI_ID", do.label = TRUE, pt.size = 1))

Loading the second experiment

par(mfrow=c(1,1))
par(las=2)
par(mar=c(5,15,3,3))
barplot(rowSums(hto2), main = "sequenced HTO distribution", horiz=TRUE)

rowSums(hto2)
##   Spleen-MP    Spleen-M Spleen-ctrl    Spleen-P   Thymus-MP    Thymus-M 
##     3569690     1668124      929963     3251282      484276      749822 
## Thymus-ctrl    Thymus-P 
##      310711     1203239

Demultiplexing results

Cells classification

print (table(hashtag2@meta.data$MULTI_ID))
## 
##     Doublet    Negative Spleen-ctrl    Spleen-M   Spleen-MP    Spleen-P 
##        2254         427         824        1294        2589        1689 
## Thymus-ctrl    Thymus-M   Thymus-MP    Thymus-P 
##         461        1266         522        1664

Violinplot (features)

VlnPlot(hashtag2,features = c("nFeature_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

Violin plots (HTO counts)

VlnPlot(hashtag2,features = c("nCount_HTO"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

tSNEs based on HTO

# Calculate a distance matrix using HTO
hto.dist.mtx <- as.matrix(dist(t(GetAssayData(object = hashtag2, assay = "HTO"))))

# Calculate tSNE embeddings with a distance matrix
hashtag2 <- RunTSNE(hashtag2, distance.matrix = hto.dist.mtx, perplexity = 100)
DimPlot(hashtag2, group.by = "MULTI_ID")

HTO margin

Tsne<-data.frame(
  tSNE_1 = hashtag2@reductions$tsne@cell.embeddings[,1],
  tSNE_2= hashtag2@reductions$tsne@cell.embeddings[,2],
  gene= hashtag2@meta.data$HTO_margin
)

HTO= hashtag2@meta.data$MULTI_ID
Max=max(hashtag2@meta.data$HTO_margin)
Min=min(hashtag2@meta.data$HTO_margin)

ggplot(Tsne,aes(x=tSNE_1,y=tSNE_2))+geom_point(aes(color=gene,shape=HTO))+
           scale_colour_gradient2(low = "blue",mid="orange",high="red",name="HTO margin",midpoint=(Max+Min)/2)+scale_shape_manual(values = c(15,16,17,18,19,20,21,22,23,24,25))

Ridge plots

Visualize enrichment for selected HTOs with ridge plots

RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[1:2],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[3:4],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[5:6],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[7:8],ncol = 2, group.by = "MULTI_ID")

Sample Information

The analysis will be run on the sample 2 (190211).

During the sample loading, we filter cells that do not pass the following filters.

Used parameters in the Seurat CreateSeuratObject function: * min.genes: 3 . Include cells where at least 3 genes are detected * min.cells: 200 . Include genes with detected expression in at least 200 cells

After those filters, the remaining cell number is `r length(colnames(hashtag2@assays$RNA@data)).

#add Exp2 cell identity
HTO_cr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID == "Spleen-ctrl" ))
HTO_ct2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-ctrl" ))
HTO_mr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-M" ))
HTO_mt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-M" ))
HTO_pr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-P" ))
HTO_pt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-P" ))
HTO_pmr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-MP" ))
HTO_pmt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-MP" ))
HTO_d2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Doublet" ))
HTO_n2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Negative" ))

HTO_thymus2 = c(HTO_ct2,HTO_mt2,HTO_pt2,HTO_pmt2)
HTO_spleen2 = c(HTO_cr2,HTO_mr2,HTO_pr2,HTO_pmr2)
HTO_identified2 = c(HTO_thymus2, HTO_spleen2)

# Create a Seurat object without doublet and unassigned cells (remove "negative", "doublet" & "nothing"))
clean.subset2 <- subset(x = hashtag2, cells = HTO_identified2)
VlnPlot(clean.subset2,features = c("nFeature_RNA", "nCount_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

After removing doublets and negative cells, the remaining cell number is `r length(colnames(clean.subset2@assays$RNA@data)).

Adding ADT

# Load in the UMI matrix
umi <- GetAssayData(object = clean.subset2, slot = "counts")

# Load in the ADT count matrix
raw.adt <- Read10X(PATH_ADT_DATA2, gene.column = 1)
adt <- raw.adt[c(1:6),]

rownames(adt) <- c("CD4","CD5","CD8","CD25","CD44","CD69")

#create an empty matrix containing NAs
Cell.list <- colnames(GetAssayData(object = clean.subset2[["RNA"]], slot = "data" ) )
ADT.list <- c(unique(rownames(adt)))
mat.adt <- matrix(nrow = length(ADT.list), ncol = length(Cell.list))
rownames(mat.adt) = ADT.list
colnames(mat.adt) = Cell.list

# Get cell barcodes detected by both RNA and ADT
joint_bcs <- intersect(colnames(umi),colnames(adt))
adt <- as.matrix(adt[,joint_bcs])

# Fill the empty matrix with values when existing
mat.adt[,joint_bcs]<-adt[,joint_bcs]

# Add ADT data as a new assay independent from RNA
clean.subset2[["ADT"]] <- CreateAssayObject(counts = mat.adt[,colnames(clean.subset2)])

# Normalize ADT data, here we use centered log-ratio (CLR) transformation
clean.subset2 <- NormalizeData(clean.subset2, assay = "ADT", normalization.method = "CLR")

#Scale
clean.subset2 <- ScaleData(clean.subset2, assay = "ADT")

ADT list :

print (rownames(adt))

[1] “CD4” “CD5” “CD8” “CD25” “CD44” “CD69”

Mitochondrial percentage versus nFeatures

df<-data.frame(multi.id=Seurat2@misc$old_meta_data$MULTI_ID,percent.mito=Seurat2@misc$old_meta_data$percent.mito,nFeature_RNA=Seurat2@misc$old_meta_data$nFeature_RNA)
ggplotly(ggplot(df,aes(x=nFeature_RNA,y=percent.mito,color=multi.id))+geom_point())

UMAP:

ggplotly(DimPlot(Seurat2, reduction = "umap", group.by = "MULTI_ID", do.label = TRUE, pt.size = 1))

Merging our two experiments

Load separate R object

You can load objects done with the code above. Or our object ?? (link )

Integrating the 2 seurat objects with seurat integration (cca)

We identified r length(gene1) expressed in sample1 and r length(gene2) expressed in sample2. r length(common_genes) are in common in this two set and will the integrated in the merged and corrected object.

UMAP:

Analysis part

Sample Information

The analysis will be run on the sample MYC_PTEN_01 (181031 & 190211).

During the sample loading, we filter cells that do not pass the following filters. We also filter cells that are detected as human/mouse multiplet using their barcodes.
Here are the description of those parameters in the Seurat CreateSeuratObject function:

  • min.genes: Include cells where at least this many genes are detected
  • min.cells: Include genes with detected expression in at least this many cells

After those filters, and merging MYC_PTEN_01 and MYC_PTEN02 the remaining cell number is r length(colnames(exp1.2.integrated)).

UMAP and clustering parameter

Merge checking

HTO

Orig.idents

T-cell selection

According to T-cell markers we will exclude Cd3d low clusters: 13 (Bcells), 11, 14, 17 (monocytes/macrophages). According to T-cell markers we will exclude Cd3d/Cd3e low clusters: 11 (Bcells), 13, 17, 18, 19 (monocytes/macrophages), 16, 14 (ILC/NK).

Known RNA B and T markers

re clustering

T-cell umaps

HTO

clustering

END OF PREPROCESSING

We obtain the final object with clustering to start the analysis

sessionInfo()
## R version 3.5.3 (2019-03-11)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib/libopenblasp-r0.2.18.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] knitr_1.23         RColorBrewer_1.1-2 magrittr_1.5      
## [4] dplyr_0.8.1        gridExtra_2.3      kableExtra_1.1.0  
## [7] plotly_4.9.0       ggplot2_3.1.1      Seurat_3.0.1      
## 
## loaded via a namespace (and not attached):
##   [1] nlme_3.1-140        tsne_0.1-3          bitops_1.0-6       
##   [4] webshot_0.5.1       httr_1.4.0          sctransform_0.2.0  
##   [7] tools_3.5.3         R6_2.4.0            irlba_2.3.3        
##  [10] KernSmooth_2.23-15  lazyeval_0.2.2      colorspace_1.4-1   
##  [13] npsurv_0.4-0        withr_2.1.2         tidyselect_0.2.5   
##  [16] compiler_3.5.3      rvest_0.3.4         xml2_1.2.0         
##  [19] labeling_0.3        caTools_1.17.1.2    scales_1.0.0       
##  [22] lmtest_0.9-37       readr_1.3.1         ggridges_0.5.1     
##  [25] pbapply_1.4-0       stringr_1.4.0       digest_0.6.19      
##  [28] rmarkdown_1.12      R.utils_2.8.0       base64enc_0.1-3    
##  [31] pkgconfig_2.0.2     htmltools_0.3.6     bibtex_0.4.2       
##  [34] highr_0.8           htmlwidgets_1.3     rlang_0.3.4        
##  [37] rstudioapi_0.10     shiny_1.3.2         zoo_1.8-5          
##  [40] jsonlite_1.6        crosstalk_1.0.0     ica_1.0-2          
##  [43] gtools_3.8.1        R.oo_1.22.0         Matrix_1.2-17      
##  [46] Rcpp_1.0.1          munsell_0.5.0       ape_5.3            
##  [49] reticulate_1.12     R.methodsS3_1.7.1   stringi_1.4.3      
##  [52] yaml_2.2.0          gbRd_0.4-11         MASS_7.3-51.4      
##  [55] gplots_3.0.1.1      Rtsne_0.15          plyr_1.8.4         
##  [58] grid_3.5.3          promises_1.0.1      parallel_3.5.3     
##  [61] gdata_2.18.0        listenv_0.7.0       ggrepel_0.8.1      
##  [64] crayon_1.3.4        lattice_0.20-38     cowplot_0.9.4      
##  [67] splines_3.5.3       hms_0.4.2           SDMTools_1.1-221.1 
##  [70] pillar_1.4.0        igraph_1.2.4.1      future.apply_1.2.0 
##  [73] reshape2_1.4.3      codetools_0.2-16    glue_1.3.1         
##  [76] evaluate_0.13       lsei_1.2-0          metap_1.1          
##  [79] data.table_1.12.2   httpuv_1.5.1        png_0.1-7          
##  [82] Rdpack_0.11-0       gtable_0.3.0        RANN_2.6.1         
##  [85] purrr_0.3.2         tidyr_0.8.3         future_1.13.0      
##  [88] assertthat_0.2.1    xfun_0.7            mime_0.6           
##  [91] rsvd_1.0.0          xtable_1.8-4        later_0.8.0        
##  [94] survival_2.44-1.1   viridisLite_0.3.0   tibble_2.1.1       
##  [97] cluster_2.0.9       globals_0.12.4      fitdistrplus_1.0-14
## [100] ROCR_1.0-7
---
title: "Experiment_Preprocessing"
author: "Delphine Potier / Mathis Nozais / Saran Pankaew"
output:
  html_document:
    code_folding: hide
    code_download: true
---


<style type="text/css">
.main-container {
  max-width: 1800px;
  margin-left: auto;
  margin-right: auto;
}
</style>

```{r global-options, include=FALSE}
knitr::opts_chunk$set(warning=FALSE, message=FALSE,fig.align = 'center')
```


#to do before publish
```{r}
#change all path to data showing my environment (NAS...) mettre lien à partir de Workspace ou il dowloaderont les fichier source (fastq et objet R)
#ajouter un test pour etre sur que y'ai subset 1 et 2 et pas que un des deux 

#do we want to show the code in rmd ? 

# GO mise en ligne des fichier brut

#test final sans aucun objet, avec le premier obj et avec le deuxieme

#change output path
```



```{r env_loading, include=FALSE}
# Load packages, data and functions
library(Seurat)
library(plotly)
library(kableExtra)
library(ggplot2)
library(gridExtra)

#Path to the analysis folder
WORKSPACE <- "/home/nozaism/Workspace/01_These/01_Project/Myc_Pten_Paper/Myc_repo/"
# Path to the folder containing scripts used in the analysis
CWD <- "/home/nozaism/Workspace/Function_delphine/" 
# Load the R scripts containing the functions used in the analysis
source(paste(CWD, "Workflow_functions_S3.R", sep="/"))
#  Path to the folder that will contain output objects
#OUTPUT_PATH <- (paste0(WORKSPACE,"02_Seurat_analysis/02_Output/"))
OUTPUT_PATH <- ("/home/nozaism/Workspace/01_These/01_Project/Myc_Pten_Paper/Output_repo/")
# Set the random number seed
set.seed(1234)
# Resolution parameter for Seurat clustering
RESOLUTION <- 1
```

```{r, echo=FALSE,results='asis'}
SAMPLE1 <- "181031"
SAMPLE2 <- "190211"

if(! file.exists(paste0(OUTPUT_PATH, "Seurat_clean-subset2_tomerge_", SAMPLE2, ".Robj"))){
print("You are starting the analysis from count matrix obtain with CellRanger and Cite-seq-count")
part1 <- TRUE #experiment one by one
part2 <- TRUE #merging 2 object
}else if( file.exists(paste0(OUTPUT_PATH, "T-Seurat-merged_clean-subset",".Robj"))){
print("You already have the final object of preprocessing, you can now lauch the Experiment_analysis script")
part1 <- FALSE
part2 <- FALSE
}else{ 
print ("You are starting analysis from our two replicate Robj in order to do the integration")
part1 <- FALSE
part2 <- TRUE
}

```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
# Loading the first experiment
```


```{r path1_loading, include=FALSE,eval=(part1 == TRUE )}
# Load path for files
PATH_MOUSE_DATA1 <- "/mnt/NAS6/BNlab/Saran/Rerun_CellRanger/MYC_PTEN_01_mm10/outs/filtered_feature_bc_matrix/"
PROJECT_NAME1 <- paste("10X_", SAMPLE1, sep = "")
PATH_HTO_DATA1 <- "/mnt/NAS5/BNlab/delphine/barecoded_scRNAseq/MYC_PTEN/CITE-seq-count_181031_Result_hd2/umi_count/"
```

```{r Sample1_loading, include=FALSE,eval=(part1 == TRUE)}
# Create Seurat object and apply filtering   
# Read 10X data
mouse_data1 <- Read10X(data.dir = PATH_MOUSE_DATA1)

# Create the Seurat object and first filter
Not_processed_Seurat_m1 <- CreateSeuratObject(counts = mouse_data1, min.cells = 3, min.features = 200, project = "181031")
```

```{r HTO1_loading, include=FALSE,eval=(part1 == TRUE)}
# Load in the UMI matrix
umi_sparse1 <- GetAssayData(object = Not_processed_Seurat_m1, slot = "counts")

# Load in the HTO count matrix
raw.hto1 <- Read10X(PATH_HTO_DATA1, gene.column = 1)
hto1 <- raw.hto1[c(1:8),]

rownames(hto1) <- c("Spleen-MP","Spleen-M","Spleen-ctrl","Spleen-P","Thymus-MP","Thymus-M","Thymus-ctrl","Thymus-P")

# Select cell barcodes detected by both RNA and HTO
# In the example datasets we have already filtered the cells for you, but perform this step for clarity.
joint_bcs1 <- intersect(colnames(umi_sparse1),colnames(hto1))

# Subset RNA and HTO counts by joint cell barcodesumi_sparse <- pbmc_umi_sparse[,joint_bcs]
hto1 <- as.matrix(hto1[,joint_bcs1])

# Confirm that the HTO have the correct names
print (rownames(hto1))
```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
Looking to HTO distribution accross sample :
```

```{r HTO distribution,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
#HTO distribution
par(mfrow=c(1,1))
par(las=2)
par(mar=c(5,15,3,3))
barplot(rowSums(hto1), main = "sequenced HTO distribution", horiz=TRUE)
rowSums(hto1)
```

```{r, message=FALSE, include=FALSE,eval=(part1 == TRUE)}
### Setup seurat object and add in the hto data
# Setup Seurat object
hashtag1 <- CreateSeuratObject(counts = umi_sparse1[,joint_bcs1], assay = "RNA", project = "181031")

# Normalize RNA data with log normalization
hashtag1 <- NormalizeData(hashtag1,display.progress = FALSE)
# Find and scale variable genes
hashtag1 <- FindVariableFeatures(hashtag1, part1.plot = F, selection.method = "vst", nfeatures = 2000, display.progress = FALSE)
hashtag1 <- ScaleData(hashtag1,genes.use = hashtag1@var.features,display.progress = FALSE)
```

```{r, message=FALSE, include=FALSE,eval=(part1 == TRUE)}
### Adding HTO data as an independent assay

# Add HTO data as a new assay independent from RNA
hashtag1[["HTO"]] <- CreateAssayObject(counts = hto1)
hashtag1 <- SetAssayData(hashtag1,assay = "HTO",slot = "counts",new.data = hto1)
# Normalize HTO data, here we use centered log-ratio (CLR) transformation
hashtag1 <- NormalizeData(hashtag1, assay = "HTO",normalization.method = "CLR",display.progress = FALSE)
```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
# Demultiplexing HTO
```


```{r Demultiplexing, message=FALSE, include=FALSE,eval=(part1 == TRUE)}
#Demultiplex cells based on HTO enrichment
#Run HTOdemux just to get the HTOmax_ID fied
hashtag1 <- HTODemux(hashtag1, assay = "HTO", positive.quantile = 0.99, verbose = FALSE)
#Here we use the Seurat function MULTIseqDemux() to assign single cells back to their sample origins.
hashtag1 <- MULTIseqDemux(hashtag1, assay = "HTO",autoThresh = TRUE, maxiter = 10,qrange = seq(from = 0.1, to = 0.9, by = 0.05), verbose = TRUE)
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
##Demultiplexing results {.tabset}
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
###Cells classification
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
print (table(hashtag1@meta.data$MULTI_ID))
```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Violinplot (features)
```


```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
VlnPlot(hashtag1,features = c("nFeature_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Violinplot (HTO counts)
```


```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
VlnPlot(hashtag1,features = c("nCount_HTO"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
## tSNEs based on HTO
```


```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
# Calculate a distance matrix using HTO
hto.dist.mtx <- as.matrix(dist(t(GetAssayData(object = hashtag1, assay = "HTO"))))

# Calculate tSNE embeddings with a distance matrix
hashtag1 <- RunTSNE(hashtag1, distance.matrix = hto.dist.mtx, perplexity = 100)
```

```{r, message=FALSE,echo=FALSE,eval=(part1 == TRUE)}
DimPlot(hashtag1, group.by = "MULTI_ID",reduction = "tsne")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### HTO margin
```


```{r, fig.width = 8, fig.height = 7, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
Tsne<-data.frame(
  tSNE_1 = hashtag1@reductions$tsne@cell.embeddings[,1],
  tSNE_2= hashtag1@reductions$tsne@cell.embeddings[,2],
  gene= hashtag1@meta.data$HTO_margin
)

HTO= hashtag1@meta.data$MULTI_ID
Max=max(hashtag1@meta.data$HTO_margin)
Min=min(hashtag1@meta.data$HTO_margin)
ggplot(Tsne,aes(x=tSNE_1,y=tSNE_2))+geom_point(aes(color=gene,shape=HTO))+
           scale_colour_gradient2(low = "blue",mid="orange",high="red",name="HTO margin",midpoint=(Max+Min)/2)+scale_shape_manual(values = c(15,16,17,18,19,20,21,22,23,24,25))
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Ridge plots

**Visualize enrichment for selected HTOs with ridge plots**
```


```{r, fig.height = 4, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[1:2],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[3:4],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[5:6],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[7:8],ncol = 2, group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
## Sample Information

The analysis will be run on the sample 1 (181031).

During the sample loading, we filter cells that do not pass the following filters.   
Here are the description of those parameters in the Seurat *CreateSeuratObject* function:
  
* min.genes: Include cells where at least 200 genes are detected
* min.cells: Include genes with detected expression in at least 3 cells

After those filters, the remaining cell number is **`r length(colnames(hashtag1@assays$RNA@data))**.
```

```{r doublet_negative_removal, results='asis',eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
#add Exp1 cell identity
HTO_cr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-ctrl" ))
HTO_ct1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-ctrl" ))
HTO_mr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-M" ))
HTO_mt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-M" ))
HTO_pr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-P" ))
HTO_pt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-P" ))
HTO_pmr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-MP" ))
HTO_pmt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-MP" ))
HTO_d1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Doublet" ))
HTO_n1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Negative" ))

HTO_thymus1 = c(HTO_ct1,HTO_mt1,HTO_pt1,HTO_pmt1)
HTO_spleen1 = c(HTO_cr1,HTO_mr1,HTO_pr1,HTO_pmr1)
HTO_identified1 = c(HTO_thymus1, HTO_spleen1)


# Create a Seurat object without doublet and unassigned cells (remove "negative", "doublet" & "nothing"))
clean.subset1 <- subset(x = hashtag1, cells = HTO_identified1)
VlnPlot(clean.subset1,features = c("nFeature_RNA", "nCount_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
After removing doublets and negative cells, the remaining cell number is **`r length(colnames(clean.subset1@assays$RNA@data))**.
```


```{r processing_1, include=FALSE,eval=(part1 == TRUE)}
# OBJECT ONE SAVE

if(! file.exists(paste0(OUTPUT_PATH, "Seurat_clean-subset1_tomerge_", SAMPLE1, ".Robj"))){
  #1- QC (fait à partir de la sous sélection)
  Seurat1 <- QC_function_mito_threshold(Seurat = clean.subset1, mito_threshold = 0.1, do_plot = FALSE)
  
  #2- Find variable genes
  Seurat1 <- FindVariableFeatures(object = Seurat1, 
                                  assay = "RNA", selection.method = "vst", nfeatures = 2000,
                                  verbose = FALSE, do.plot=TRUE)
  
  Seurat1 <- ScaleData(Seurat1, 
                       assay="RNA",
                       verbose = FALSE, 
                       #do.scale = FALSE, 
                       do.center = TRUE)
  
  Seurat1 <- RunPCA(object = Seurat1,
                    assay = "RNA",
                    verbose = FALSE, #if TRUE print the top genes for each PC
                    features =  VariableFeatures(object = Seurat1), 
                    seed.use = 1234,
                    npcs = 50) # sur les 50 premieres composantes
  
  ElbowPlot(Seurat1, ndims = 50, reduction = "pca")
  
  
  Seurat1 <- ProjectDim(object = Seurat1,
                        nfeatures.print = 20,
                        dims.print = 1:10)
  
  Seurat1 <- RunTSNE(object = Seurat1,
                     do.fast = TRUE, 
                     seed.use = 1234,
                     dims = 1:20, # Uses 20 first PCs
                     perplexity = 40)
  
  Seurat1 <- FindNeighbors(object = Seurat1, 
                           dims = 1:20 , 
                           verbose = FALSE, 
                           force.recalc = TRUE, 
                           reduction = "pca")
  
  Seurat1 <- FindClusters(object = Seurat1, 
                          resolution = RESOLUTION,
                          verbose = FALSE,
                          random.seed = 1234)
  
  Seurat1 <- RunUMAP(object = Seurat1, reduction = "pca", seed.use = 1234, dims = 1:20)
  
  save(Seurat1, file = paste0(OUTPUT_PATH, "Seurat_clean-subset1_tomerge_", SAMPLE1, ".Robj"))
}else{
  load(paste0(OUTPUT_PATH, "Seurat_clean-subset1_tomerge_", SAMPLE1, ".Robj"))
}
```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
## Mitochondrial percentage versus nFeatures
```

```{r mito_vs_nfeatures,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
df<-data.frame(multi.id=Seurat1@misc$old_meta_data$MULTI_ID,percent.mito=Seurat1@misc$old_meta_data$percent.mito,nFeature_RNA=Seurat1@misc$old_meta_data$nFeature_RNA)
ggplotly(ggplot(df,aes(x=nFeature_RNA,y=percent.mito,color=multi.id))+geom_point())
```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
## UMAP
```

```{r UMAP_HTO_seurat_1,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
ggplotly(DimPlot(Seurat1, reduction = "umap", group.by = "MULTI_ID", do.label = TRUE, pt.size = 1))
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
# Loading the second experiment
```


```{r path2_loading, include=FALSE,eval=(part1 == TRUE)}
# Load path for files
PATH_MOUSE_DATA2 <- "/mnt/NAS6/BNlab/Saran/Rerun_CellRanger/MYC_PTEN_02_mm10/outs/filtered_feature_bc_matrix/"
PROJECT_NAME2 <- paste("10X_", SAMPLE2, sep = "")
PATH_HTO_DATA2 <- "/mnt/NAS6/BNlab/mathis/scRNAseq/DMATh3/CITE-seq-count141_190211_Result_hashtag_hd2/umi_count/"
PATH_ADT_DATA2 <- "/mnt/NAS5/BNlab/delphine/barecoded_scRNAseq/MYC_PTEN2/CITE-seq-count141_190211_ADT-Result_hd2/umi_count"
```

```{r Sample_loading_2, include=FALSE,eval=(part1 == TRUE)}
# Create Seurat object and apply filtering   
# Read 10X data
mouse_data2 <- Read10X(data.dir = PATH_MOUSE_DATA2)


# Create the Seurat object and first filter
Not_processed_Seurat_m2 <- CreateSeuratObject(counts = mouse_data2, min.cells = 3, min.features = 200, project = "190211")
```

```{r , message=FALSE, include=FALSE,eval=(part1 == TRUE)}
# Load in the UMI matrix
umi_sparse2 <- GetAssayData(object = Not_processed_Seurat_m2, slot = "counts")

# Load in the HTO count matrix
raw.hto2 <- Read10X(PATH_HTO_DATA2, gene.column = 1)
hto2 <- raw.hto2[c(1:8),]

rownames(hto2) <- c("Spleen-MP","Spleen-M","Spleen-ctrl","Spleen-P","Thymus-MP","Thymus-M","Thymus-ctrl","Thymus-P")

# Select cell barcodes detected by both RNA and HTO
joint_bcs2 <- intersect(colnames(umi_sparse2),colnames(hto2))
hto2 <- as.matrix(hto2[,joint_bcs2])

# Confirm that the HTO have the correct names
print (rownames(hto2))
```

```{r,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
par(mfrow=c(1,1))
par(las=2)
par(mar=c(5,15,3,3))
barplot(rowSums(hto2), main = "sequenced HTO distribution", horiz=TRUE)

rowSums(hto2)
```

```{r, message=FALSE, include=FALSE,eval=(part1 == TRUE)}
### Setup seurat object and add in the hto data
# Setup Seurat object
hashtag2 <- CreateSeuratObject(counts = umi_sparse2[,joint_bcs2 ], assay = "RNA", project = "190211")

# Normalize RNA data with log normalization
hashtag2 <- NormalizeData(hashtag2,display.progress = FALSE)
# Find and scale variable genes
hashtag2 <- FindVariableFeatures(hashtag2,do.plot = F,selection.method = "vst", nfeatures = 2000, display.progress = FALSE)
hashtag2 <- ScaleData(hashtag2,genes.use = hashtag2@assays$RNA@var.features,display.progress = FALSE)
```

```{r, message=FALSE, include=FALSE,eval=(part1 == TRUE)}
### Adding HTO data as an independent assay
# Add HTO data as a new assay independent from RNA
hashtag2[["HTO"]] <- CreateAssayObject(counts = hto2)
hashtag2 <- SetAssayData(hashtag2,assay = "HTO",slot = "counts",new.data = hto2)
# Normalize HTO data, here we use centered log-ratio (CLR) transformation
hashtag2 <- NormalizeData(hashtag2, assay = "HTO",normalization.method = "CLR",display.progress = FALSE)
```

```{r, message=FALSE, include=FALSE,eval=(part1 == TRUE)}
#Run HTOdemux just to get the HTO_maxID field
hashtag2 <- HTODemux(hashtag2, assay = "HTO", positive.quantile = 0.99, verbose = FALSE)
#Demultiplex cells based on HTO enrichment
hashtag2 <- MULTIseqDemux(hashtag2, assay = "HTO",autoThresh = TRUE, maxiter = 10,qrange = seq(from = 0.1, to = 0.9, by = 0.05), verbose = TRUE)
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
##Demultiplexing results {.tabset}
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
###Cells classification
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
print (table(hashtag2@meta.data$MULTI_ID))
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Violinplot (features)
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
VlnPlot(hashtag2,features = c("nFeature_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Violin plots (HTO counts)
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
VlnPlot(hashtag2,features = c("nCount_HTO"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
## tSNEs based on HTO
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
# Calculate a distance matrix using HTO
hto.dist.mtx <- as.matrix(dist(t(GetAssayData(object = hashtag2, assay = "HTO"))))

# Calculate tSNE embeddings with a distance matrix
hashtag2 <- RunTSNE(hashtag2, distance.matrix = hto.dist.mtx, perplexity = 100)
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
DimPlot(hashtag2, group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### HTO margin
```


```{r, fig.width = 8, fig.height = 7, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
Tsne<-data.frame(
  tSNE_1 = hashtag2@reductions$tsne@cell.embeddings[,1],
  tSNE_2= hashtag2@reductions$tsne@cell.embeddings[,2],
  gene= hashtag2@meta.data$HTO_margin
)

HTO= hashtag2@meta.data$MULTI_ID
Max=max(hashtag2@meta.data$HTO_margin)
Min=min(hashtag2@meta.data$HTO_margin)

ggplot(Tsne,aes(x=tSNE_1,y=tSNE_2))+geom_point(aes(color=gene,shape=HTO))+
           scale_colour_gradient2(low = "blue",mid="orange",high="red",name="HTO margin",midpoint=(Max+Min)/2)+scale_shape_manual(values = c(15,16,17,18,19,20,21,22,23,24,25))
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Ridge plots

**Visualize enrichment for selected HTOs with ridge plots**
```


```{r, fig.height = 4, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[1:2],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[3:4],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[5:6],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[7:8],ncol = 2, group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}

## Sample Information

The analysis will be run on the sample 2 (190211).

During the sample loading, we filter cells that do not pass the following filters.

Used parameters in the Seurat *CreateSeuratObject* function:
* min.genes: 3 . Include cells where at least 3 genes are detected
* min.cells: 200 . Include genes with detected expression in at least 200 cells

After those filters, the remaining cell number is **`r length(colnames(hashtag2@assays$RNA@data))**.
```


```{r cell_select_2, results='asis',eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
#add Exp2 cell identity
HTO_cr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID == "Spleen-ctrl" ))
HTO_ct2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-ctrl" ))
HTO_mr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-M" ))
HTO_mt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-M" ))
HTO_pr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-P" ))
HTO_pt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-P" ))
HTO_pmr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-MP" ))
HTO_pmt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-MP" ))
HTO_d2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Doublet" ))
HTO_n2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Negative" ))

HTO_thymus2 = c(HTO_ct2,HTO_mt2,HTO_pt2,HTO_pmt2)
HTO_spleen2 = c(HTO_cr2,HTO_mr2,HTO_pr2,HTO_pmr2)
HTO_identified2 = c(HTO_thymus2, HTO_spleen2)

# Create a Seurat object without doublet and unassigned cells (remove "negative", "doublet" & "nothing"))
clean.subset2 <- subset(x = hashtag2, cells = HTO_identified2)
VlnPlot(clean.subset2,features = c("nFeature_RNA", "nCount_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
After removing doublets and negative cells, the remaining cell number is **`r length(colnames(clean.subset2@assays$RNA@data))**.
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
## Adding ADT
```


```{r load_adt,  message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
# Load in the UMI matrix
umi <- GetAssayData(object = clean.subset2, slot = "counts")

# Load in the ADT count matrix
raw.adt <- Read10X(PATH_ADT_DATA2, gene.column = 1)
adt <- raw.adt[c(1:6),]

rownames(adt) <- c("CD4","CD5","CD8","CD25","CD44","CD69")

#create an empty matrix containing NAs
Cell.list <- colnames(GetAssayData(object = clean.subset2[["RNA"]], slot = "data" ) )
ADT.list <- c(unique(rownames(adt)))
mat.adt <- matrix(nrow = length(ADT.list), ncol = length(Cell.list))
rownames(mat.adt) = ADT.list
colnames(mat.adt) = Cell.list

# Get cell barcodes detected by both RNA and ADT
joint_bcs <- intersect(colnames(umi),colnames(adt))
adt <- as.matrix(adt[,joint_bcs])

# Fill the empty matrix with values when existing
mat.adt[,joint_bcs]<-adt[,joint_bcs]

# Add ADT data as a new assay independent from RNA
clean.subset2[["ADT"]] <- CreateAssayObject(counts = mat.adt[,colnames(clean.subset2)])

# Normalize ADT data, here we use centered log-ratio (CLR) transformation
clean.subset2 <- NormalizeData(clean.subset2, assay = "ADT", normalization.method = "CLR")

#Scale
clean.subset2 <- ScaleData(clean.subset2, assay = "ADT")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
ADT list :
```


```{r , results='asis',eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
print (rownames(adt))
```

```{r Significant_PC, include=FALSE,eval=(part1 == TRUE)}
# Traitement de l'objet
if(! file.exists(paste0(OUTPUT_PATH, "Seurat_clean-subset2_tomerge_", SAMPLE2, ".Robj"))){
  #1- QC 
  Seurat2 <- QC_function_mito_threshold(Seurat = clean.subset2, mito_threshold = 0.1, do_plot = FALSE)
  
  #2- Find variable genes
  Seurat2 <- FindVariableFeatures(object = Seurat2, 
                                  assay = "RNA",
                                  selection.method = "vst", nfeatures = 2000, verbose = FALSE, do.plot=TRUE)
  
  Seurat2 <- ScaleData(Seurat2, 
                       assay="RNA",
                       verbose = FALSE, 
                       #do.scale = FALSE, 
                       do.center = TRUE)
  
  Seurat2 <- RunPCA(object = Seurat2,
                    assay = "RNA",
                    verbose = FALSE, #if TRUE print the top genes for each PC
                    features =  VariableFeatures(object = Seurat2), 
                    seed.use = 1234,
                    npcs = 50) 
  
  ElbowPlot(Seurat2, ndims = 40, reduction = "pca")
  
  Seurat <- ProjectDim(object = Seurat2,
                       nfeatures.print = 20,
                       dims.print = 1:10)

  Seurat2 <- RunTSNE(object = Seurat2,
                     do.fast = TRUE, 
                     seed.use = 1234,
                     dims = 1:20, 
                     perplexity = 40)
  
  Seurat2 <- FindNeighbors(object = Seurat2, 
                           dims = 1:20 , 
                           verbose = FALSE, 
                           force.recalc = TRUE, 
                           reduction = "pca")
  
  Seurat2 <- FindClusters(object = Seurat2, 
                          resolution = RESOLUTION,
                          verbose = FALSE,
                          random.seed = 1234)
  
  Seurat2 <- RunUMAP(object = Seurat2, reduction = "pca", seed.use = 1234, dims = 1:20)
  
  save(Seurat2, file = paste0(OUTPUT_PATH, "Seurat_clean-subset2_tomerge_", SAMPLE2, ".Robj"))
}else{
  load(paste0(OUTPUT_PATH, "Seurat_clean-subset2_tomerge_", SAMPLE2, ".Robj"))
}
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
## Mitochondrial percentage versus nFeatures
```

```{r mito_vs_nfeatures2,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
df<-data.frame(multi.id=Seurat2@misc$old_meta_data$MULTI_ID,percent.mito=Seurat2@misc$old_meta_data$percent.mito,nFeature_RNA=Seurat2@misc$old_meta_data$nFeature_RNA)
ggplotly(ggplot(df,aes(x=nFeature_RNA,y=percent.mito,color=multi.id))+geom_point())
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
## UMAP:
```

```{r UMAP_HTO_seurat_2,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
ggplotly(DimPlot(Seurat2, reduction = "umap", group.by = "MULTI_ID", do.label = TRUE, pt.size = 1))
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
#Merging our two experiments
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
## Load separate R object
You can load objects done with the code above.
Or our object ?? (link )
```

```{r Samples_loading,eval=(part2 == TRUE ) ,include=FALSE}
#load all seurat objects built previously
load(paste0(OUTPUT_PATH, "Seurat_clean-subset1_tomerge_", SAMPLE1, ".Robj"))
load(paste0(OUTPUT_PATH, "Seurat_clean-subset2_tomerge_", SAMPLE2, ".Robj"))
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
## Integrating the 2 seurat objects with seurat integration (cca)
```

```{r, CCA, eval=(part2 == TRUE ),include=FALSE}
# Gene selection for input to CCA
FindVariableFeatures(object = Seurat1, 
        selection.method = "vst", nfeatures = 2000, verbose = FALSE)
FindVariableFeatures(object = Seurat2, 
        selection.method = "vst", nfeatures = 2000, verbose = FALSE)

exp.anchors <- FindIntegrationAnchors(object.list = c(Seurat2,Seurat1), dims = 1:30)

gene1 <- rownames(GetAssayData(Seurat1, assay = "RNA", slot = "data" ))
gene2 <- rownames(GetAssayData(Seurat2, assay = "RNA", slot = "data" ))
common_genes <- Reduce(intersect, list(gene1,gene2))

exp1.2.integrated <- IntegrateData(anchorset = exp.anchors, features.to.integrate = common_genes,dims = 1:30)
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
We identified **`r length(gene1)`** expressed in sample1 and **`r length(gene2)`** expressed in sample2. **`r length(common_genes)`** are in common in this two set and will the integrated in the merged and corrected object.


## UMAP:

# Analysis part

## Sample Information

The analysis will be run on the sample MYC_PTEN_01 (181031 & 190211).

During the sample loading, we filter cells that do not pass the following filters. We also filter cells that are detected as human/mouse multiplet using their barcodes.  
Here are the description of those parameters in the Seurat *CreateSeuratObject* function:

* min.genes: Include cells where at least this many genes are detected
* min.cells: Include genes with detected expression in at least this many cells

```

```{r filters, eval=(part2 == TRUE ),results='asis',include=FALSE}
# Affiche les parametres
Filter_parameters <- data.frame()
Filter_parameters["Value", "min.cells"] <- 3
Filter_parameters["Value", "min.genes"] <- 200
kable(Filter_parameters, "html", align = "c") %>% kable_styling(bootstrap_options = c("striped", "hover"))
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
After those filters, and merging MYC_PTEN_01 and MYC_PTEN02 the remaining cell number is **`r length(colnames(exp1.2.integrated))`**.
```



```{r cell_select,eval=(part2 == TRUE ), results='asis',include=FALSE}
### add Exp1 cell identity (181031)
#add Exp1 cell identity
HTO_cr6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Spleen-ctrl" )),"_2"),colnames(x = exp1.2.integrated))
HTO_ct6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Thymus-ctrl" )),"_2"),colnames(x = exp1.2.integrated))
HTO_mr6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Spleen-M" )),"_2"),colnames(x = exp1.2.integrated))
HTO_mt6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Thymus-M" )),"_2"),colnames(x = exp1.2.integrated))
HTO_pr6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Spleen-P" )),"_2"),colnames(x = exp1.2.integrated))
HTO_pt6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Thymus-P" )),"_2"),colnames(x = exp1.2.integrated))
HTO_pmr6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Spleen-MP" )),"_2"),colnames(x = exp1.2.integrated))
HTO_pmt6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Thymus-MP" )),"_2"),colnames(x = exp1.2.integrated))

### add Exp2 cell identity (190211) 
HTO_cr2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID == "Spleen-ctrl" )),"_1"),colnames(x = exp1.2.integrated))
HTO_ct2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Thymus-ctrl" )),"_1"),colnames(x = exp1.2.integrated))
HTO_mr2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Spleen-M" )),"_1"),colnames(x = exp1.2.integrated))
HTO_mt2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Thymus-M" )),"_1"),colnames(x = exp1.2.integrated))
HTO_pr2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Spleen-P" )),"_1"),colnames(x = exp1.2.integrated))
HTO_pt2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Thymus-P" )),"_1"),colnames(x = exp1.2.integrated))
HTO_pmr2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Spleen-MP" )),"_1"),colnames(x = exp1.2.integrated))
HTO_pmt2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Thymus-MP" )),"_1"),colnames(x = exp1.2.integrated))



exp1.2.integrated@meta.data$HTO = "nothing"
exp1.2.integrated@meta.data[HTO_cr2,]$HTO = "WT spleen"
exp1.2.integrated@meta.data[HTO_ct2,]$HTO = "WT thymus"
exp1.2.integrated@meta.data[HTO_cr6,]$HTO = "WT spleen"
exp1.2.integrated@meta.data[HTO_ct6,]$HTO = "WT thymus"
exp1.2.integrated@meta.data[HTO_pr2,]$HTO = "PTEN- spleen"
exp1.2.integrated@meta.data[HTO_pt2,]$HTO = "PTEN- thymus"
exp1.2.integrated@meta.data[HTO_pr6,]$HTO = "PTEN- spleen"
exp1.2.integrated@meta.data[HTO_pt6,]$HTO = "PTEN- thymus"
exp1.2.integrated@meta.data[HTO_mr2,]$HTO = "MYC- spleen"
exp1.2.integrated@meta.data[HTO_mt2,]$HTO = "MYC- thymus"
exp1.2.integrated@meta.data[HTO_mr6,]$HTO = "MYC- spleen"
exp1.2.integrated@meta.data[HTO_mt6,]$HTO = "MYC- thymus"
exp1.2.integrated@meta.data[HTO_pmr2,]$HTO = "Myc- PTEN- spleen"
exp1.2.integrated@meta.data[HTO_pmt2,]$HTO = "Myc- PTEN- thymus"
exp1.2.integrated@meta.data[HTO_pmr6,]$HTO = "Myc- PTEN- spleen"
exp1.2.integrated@meta.data[HTO_pmt6,]$HTO = "Myc- PTEN- thymus"

HTO_thymus = c(HTO_ct2,HTO_mt2,HTO_pt2,HTO_pmt2,HTO_ct6,HTO_mt6,HTO_pt6,HTO_pmt6)
HTO_spleen = c(HTO_cr2,HTO_mr2,HTO_pr2,HTO_pmr2,HTO_cr6,HTO_mr6,HTO_pr6,HTO_pmr6)

identified <- c(HTO_thymus,HTO_spleen)
VlnPlot(exp1.2.integrated,features = "nFeature_RNA",pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
VlnPlot(exp1.2.integrated,features = "nFeature_RNA",pt.size = 0.1, log = TRUE,  group.by = "HTO")
VlnPlot(exp1.2.integrated,features = "nFeature_RNA",pt.size = 0.1, log = TRUE,  group.by = "orig.ident")

Seurat <- exp1.2.integrated
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
## UMAP and clustering parameter
```

```{r Significant_PC_merge, eval=(part2 == TRUE ),include=FALSE}
# Traitement de l'objet
if(! file.exists(paste0(OUTPUT_PATH, "Seurat-integrated_181031_190211.Robj"))){
  
  Seurat <- ScaleData( object =  Seurat, 
                      assay="integrated",
                      verbose = FALSE,
                      #do.scale = FALSE,
                      do.center = TRUE)
  
  Seurat <- RunPCA(object = Seurat, features = VariableFeatures(Seurat), npcs = 50, seed.use = 1234, verbose = FALSE)
  
  ElbowPlot(Seurat, ndims = 40, reduction = "pca")
  
  # Scorer les genes pour les composantes
  Seurat <- ProjectDim(object = Seurat,
                  assay="integrated",
                  nfeatures.print = 20,
                  dims.print = 1:12)
  
  Seurat <- FindNeighbors(object = Seurat, 
                  dims = 1:12 , 
                  assay="integrated",
                  verbose = FALSE)#, 
                  #force.recalc = TRUE, 
                  #reduction = "pca")
  
  Seurat <- FindClusters(object = Seurat, 
                  resolution = 1,
                  assay="integrated",
                  verbose = FALSE,
                  random.seed = 1234)
    
  #To make the UMAP
  #######################
  Seurat <- RunUMAP(object = Seurat, reduction = "pca", seed.use = 1234, dims = 1:12)
  
  DimPlot(object = Seurat, reduction = "umap", group.by = "orig.ident")
  
  save(Seurat, file = paste0(OUTPUT_PATH, "Seurat-integrated_181031_190211.Robj"))
}else{
  load(paste0(OUTPUT_PATH, "Seurat-integrated_181031_190211.Robj"))
}
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
## Merge checking {.tabset}
### HTO
```

```{r,eval=(part2 == TRUE ), echo=FALSE}
ggplotly(DimPlot(Seurat, reduction = "umap", group.by = "HTO", do.label = TRUE, pt.size = 1)+
           ggtitle("UMAP colorred by HTO classification"))
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
### Orig.idents
```

```{r, eval=(part2 == TRUE ),echo=FALSE}
ggplotly(DimPlot(Seurat, reduction = "umap", group.by = "orig.ident", do.label = TRUE, pt.size = 1))
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
## T-cell selection {.tabset}
According to T-cell markers we will exclude Cd3d low clusters: 13 (Bcells), 11, 14, 17 (monocytes/macrophages).
According to T-cell markers we will exclude Cd3d/Cd3e low clusters: 11 (Bcells), 13, 17, 18, 19 (monocytes/macrophages), 16, 14 (ILC/NK).

### Known RNA B and T markers
```

```{r bt_markers_checking, eval=(part2 == TRUE ), echo=FALSE}
DimPlot(Seurat, label = T)
bcell_known_markers <- c("Cd74","Ms4a1","Cd19","Cd3d")
FeaturePlot(object = Seurat, features = bcell_known_markers, reduction = "umap",  cols = c("grey", "light blue","cyan3","cyan4","dodgerblue3","blue","mediumslateblue","purple","orchid3","red","brown","black"))
bcell_known_markers <- c("Cd14","Fcgr3","Trdc","Cd3d")
FeaturePlot(object = Seurat, features = bcell_known_markers, reduction = "umap",  cols = c("grey", "light blue","cyan3","cyan4","dodgerblue3","blue","mediumslateblue","purple","orchid3","red","brown","black"))
FeaturePlot(object = Seurat, features = c("Il2ra","Klrg1","Il7r","Rora"), reduction = "umap", order = TRUE, cols = c("grey", "light blue","cyan3","cyan4","dodgerblue3","blue","mediumslateblue","purple","orchid3","red","brown","black"))
FeaturePlot(object = Seurat, features = c("Eomes","Ncr1","Tbx21","Kit"), reduction = "umap", order = TRUE, cols = c("grey", "light blue","cyan3","cyan4","dodgerblue3","blue","mediumslateblue","purple","orchid3","red","brown","black"))
#Nrc1 = NK
#IL7R to separate ILC from NK; IL7R+ EOMES+ should be ILC1; EOMES+ IL7R- should be NK

#comparison between marker and cluster position
ggplotly(DimPlot(Seurat, reduction = "umap", do.label = TRUE, pt.size = 1))
```

```{r Tcell_selection, eval=(part2 == TRUE ),echo=FALSE}
#remove cluster 11,12,13,17 (#Bcells & macrophages)
T.Seurat <- subset(x = Seurat, idents = c("11","13","14","16","17","18","19"), invert = TRUE)
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
## re clustering
```

```{r, include=FALSE, eval=(part2 == TRUE )}
# Traitement de l'objet
if(! file.exists(paste0(OUTPUT_PATH, "T-Seurat-merged_clean-subset_", ".Robj"))){
  # Selection was already done at the integration step, even after resubsetting it is worth to reselect (https://github.com/satijalab/seurat/issues/1528). RNA should be used, but then batch is back, subsetting before integration is not good neither (better to keep cells that can be aligned)... 
  
  T.Seurat <- ScaleData( object =  T.Seurat, 
                      assay="integrated",
                      verbose = FALSE,
                      #do.scale = FALSE,
                      do.center = TRUE)
  
  T.Seurat <- RunPCA(object = T.Seurat, features = VariableFeatures(T.Seurat), npcs = 100, seed.use = 1234, verbose = FALSE)
  
  ElbowPlot(T.Seurat, ndims = 50, reduction = "pca")
  
  # Scorer les genes pour les composantes
  T.Seurat <- ProjectDim(object = T.Seurat,
                  nfeatures.print = 20,
                  dims.print = 1:10)
  
  T.Seurat <- FindNeighbors(object = T.Seurat,
                  assay = "integrated",
                  dims = 1:18 , 
                  verbose = FALSE)#, 
                  #force.recalc = TRUE, 
                  #reduction = "pca")
  
    T.Seurat <- FindClusters(object = T.Seurat, 
                  assay = "integrated",
                  resolution = 1.8,
                  verbose = FALSE,
                  random.seed = 1234)
  #To make the UMAP
  #######################
  T.Seurat <- RunUMAP(object = T.Seurat, reduction = "pca", seed.use = 1234, dims = 1:18)
  
  DimPlot(object = T.Seurat, reduction = "umap", group.by = "orig.ident")
  p1 <- DimPlot(object = T.Seurat, reduction = "umap", group.by = "orig.ident")
  p2 <- DimPlot(object = T.Seurat, reduction = "pca", group.by = "orig.ident", 
      label = TRUE, repel = TRUE) + NoLegend()
  
  grid.arrange(p1,p2,nrow = 1, ncol =2, newpage=TRUE)

  save(T.Seurat, file = paste0(OUTPUT_PATH, "T-Seurat-merged_clean-subset", ".Robj"))
}else{
  load(paste0(OUTPUT_PATH, "T-Seurat-merged_clean-subset", ".Robj"))
}

```

```{r , eval=(part2 == TRUE ),echo=FALSE}
### add Exp2 cell identity (190211) 
spleen.cells <- c(row.names(subset(T.Seurat@meta.data, MULTI_ID == "Spleen-ctrl" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Spleen-M" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Spleen-MP" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Spleen-P" )))

thymus.cells <- c(row.names(subset(T.Seurat@meta.data, MULTI_ID == "Thymus-ctrl" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Thymus-M" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Thymus-MP" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Thymus-P" )))

T.Seurat@meta.data$tissue = "nothing"
T.Seurat@meta.data[spleen.cells,]$tissue = "Spleen"
T.Seurat@meta.data[thymus.cells,]$tissue = "Thymus"

DimPlot(T.Seurat)
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
## T-cell umaps {.tabset}
### HTO
```

```{r, eval=(part2 == TRUE ),echo=FALSE}
ggplotly(DimPlot(T.Seurat, group.by = "MULTI_ID"))
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
### clustering
```

```{r, eval=(part2 == TRUE ),echo=FALSE}
(DimPlot(T.Seurat, reduction = "umap", group.by = "integrated_snn_res.1.8", label = TRUE, pt.size = 1))
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
### END OF PREPROCESSING
We obtain the final object with clustering to start the analysis
```

```{r}
sessionInfo()
```

